
We propose a joint DRL-based algorithm to simultaneously improve the performance of V2I and V2V links in V2X communications in which the preassigned sub-bands to the V2I links need to be shared by V2V links. This algorithm uses DQN to solve the discrete sub-band assignment issue and employs DDPG to solve the continuous power allocation issue.

We propose a meta-based DRL algorithm by incorporating the idea of
Model-Agnostic Meta-Learning (MAML) [1]} into DRL. Different from the on-policy MAML, our algorithm focuses on solving the off-policy problem. Two-level update procedures are introduced in the meta-training stage of the proposed algorithm, which aim to train a policy with good generalization. The meta-adaptation stage is used to quickly adapt the trained policy in the new environment via a few time steps.

Extensive simulations are provided to evaluate the resource allocation performance of the proposed joint-DRL algorithm and the generalization capability of the proposed meta-based DRL algorithm in three realistic V2X communications scenarios. The results demonstrate that the proposed algorithms can efficiently solve the continuous power allocation issue and can achieve fast adaptation in the new environments.

